Machine learning-based analysis platform

Abstract

A device may receive invoice data related to multiple invoices, requisition data related to multiple requisitions, or project data related to multiple projects. The device may process the data using a feature extraction engine to identify features of the data. The device may process the data using a transformation engine to reduce a size of the data. The device may process the data using a set of machine learning models. The device may generate a set of recommendations related to at least one of: categorizing each of the multiple invoices, each of the multiple requisitions, or each of the multiple projects into one or more of multiple categories, identifying a set of possible suppliers for each of the multiple requisitions or each of the multiple projects, or identifying a set of similar projects for each of the multiple projects. The device may perform one or more actions.

What is claimed is: 
     
         1 . A method, comprising:
 receiving, by a device, data,
 wherein the data includes at least one of:
 invoice data related to multiple invoices associated with an organization, 
 requisition data related to multiple requisitions associated with the organization, or 
 project data related to multiple projects associated with the organization; 
 
   processing, by the device and after receiving the data, the data using a pre-processing technique,
 wherein the pre-processing technique includes at least one of:
 an image processing technique, or 
 a text processing technique; 
 
   processing, by the device and after processing the data using the pre-processing technique, the data using a feature extraction engine to identify features of the data;   processing, by the device and after processing the data using the feature extraction engine, the data using a transformation engine to reduce a size of the data;   processing, by the device and after processing the data using the transformation engine, the data using a set of machine learning models,
 wherein the set of machine learning models is related to at least one of:
 categorizing each of the multiple invoices associated with the invoice data, each of the multiple requisitions associated with the requisition data, or each of the multiple projects associated with the project data into one or more of multiple categories associated with operations of the organization, 
 identifying a set of possible suppliers for each of the multiple requisitions associated with the requisition data or each of the multiple projects associated with the project data, or 
 identifying a set of similar projects for each of the multiple projects associated with the project data; and 
 
   performing, by the device and after processing the data using the set of machine learning models, one or more actions.   
     
     
         2 . The method of  claim 1 , further comprising:
 determining a set of scores for each of the multiple invoices, each of the multiple requisitions, or each of the multiple projects based on output from the set of machine learning models,
 wherein the set of scores indicates at least one of:
 the one or more of the multiple categories into which each of the multiple invoices, each of the multiple requisitions, or each of the multiple projects are to be categorized, 
 the set of possible suppliers for each of the multiple requisitions or each of the multiple projects, or 
 the set of similar projects for each of the multiple projects. 
 
   
     
     
         3 . The method of  claim 2 , further comprising:
 generating, based on the set of scores, a set of recommendations related to at least one of:
 categorizing the multiple invoices, 
 identifying the set of possible suppliers, 
 identifying the set of similar projects, or 
 performing the one or more actions. 
   
     
     
         4 . The method of  claim 1 , wherein the set of machine learning models includes at least one of:
 a gradient boosting machine learning model, or   a generalized linear model.   
     
     
         5 . The method of  claim 1 , wherein performing the one or more actions comprises:
 generating a report that includes information identifying at least one of:
 the one or more of the multiple categories, 
 the set of possible suppliers, or 
 the set of similar projects; and 
   outputting, after generating the report, the report for display via a client device.   
     
     
         6 . The method of  claim 1 , wherein performing the one or more actions comprises:
 selecting a supplier, of the set of possible suppliers, based on a respective score associated with the set of possible suppliers,
 wherein the respective score is output from the set of machine learning models; and 
   sending, to a procurement system associated with the supplier, an electronic order for one or more items after selecting the supplier.   
     
     
         7 . The method of  claim 6 , wherein performing the one or more actions comprises:
 sending, to a vehicle associated with delivering the one or more items, information that identifies a delivery location for the one or more items.   
     
     
         8 . A device, comprising:
 one or more memories; and   one or more processors, communicatively coupled to the one or more memories, to:
 receive data,
 wherein the data is related to multiple invoices, multiple requisitions, or multiple projects associated with an organization; 
 
 process, after receiving the data, the data using a pre-processing technique,
 wherein the pre-processing technique includes at least one of:
 an image process technique, or 
 a text process technique; 
 
 
 process, after processing the data using the pre-processing technique, the data using a feature extraction engine to identify features of the data; 
 process, after processing the data using the feature extraction engine, the data using a transformation engine to reduce a size of the data; 
 process, after processing the data using the transformation engine, the data using a set of machine learning models,
 wherein the set of machine learning models is related to at least one of:
 categorizing each of the multiple invoices, each of the multiple requisitions, or each of the multiple projects into one or more of multiple categories associated with operations of the organization, 
 identifying a set of possible suppliers for each of the multiple requisitions or each of the multiple projects, or 
 identifying a set of similar projects for each of the multiple projects; 
 
 
 determine a score for the data based on output from the set of machine learning models,
 wherein the score identifies the one or more of the multiple categories, the set of possible suppliers, or the set of similar projects; and 
 
 perform, after determining the score, one or more actions. 
   
     
     
         9 . The device of  claim 8 , wherein the feature extraction engine uses, to process the data, at least one of:
 a normalization technique,   a tokenization technique,   a text-based numeric features modeling technique, or   a latent feature modeling technique.   
     
     
         10 . The device of  claim 8 , wherein the transformation engine uses, to process the data, at least one of:
 a weak classifier technique, or   a dimensionality reduction technique.   
     
     
         11 . The device of  claim 8 , wherein the one or more processors, when processing the data using the feature extraction engine, are to:
 identify a subset of the data to be analyzed based on a set of terms included in the data.   
     
     
         12 . The device of  claim 11 , wherein the one or more processors, when processing the data using the feature extraction engine, are to:
 normalize, after identifying the subset of the data, the subset of the data to a set of pre-determined terms by mapping the set of terms to the set of pre-determined terms.   
     
     
         13 . The device of  claim 8 , wherein the one or more processors, when processing the data using the transformation engine, are to:
 classify the data into one or more classifications after processing the data using the feature extraction engine,
 wherein the one or more classifications are associated with generalizing the data. 
   
     
     
         14 . The device of  claim 8 , wherein the one or more processors, when processing the data using the transformation engine, are to:
 process the data using a hash function after processing the data using the feature extraction engine,
 wherein the hash function is associated with compressing the data. 
   
     
     
         15 . A non-transitory computer-readable medium storing instructions, the instructions comprising:
 one or more instructions that, when executed by one or more processors, cause the one or more processors to:
 receive data,
 wherein the data includes at least one of:
 invoice data related to multiple invoices associated with an organization, 
 requisition data related to multiple requisitions associated with the organization, or 
 project data related to multiple projects associated with the organization; 
 
 
 process, after receiving the data, the data using a feature extraction engine to identify features of the data; 
 process, after processing the data using the feature extraction engine, the data using a transformation engine to reduce a size of the data; 
 process, after processing the data using the transformation engine, the data using a set of machine learning models; 
 generate, after processing the data using the set of machine learning models, a set of recommendations related to at least one of:
 categorizing each of the multiple invoices associated with the invoice data, each of the multiple requisitions associated with the requisition data, or each of the multiple projects associated with the project data into one or more of multiple categories associated with operations of the organization, 
 identifying a set of possible suppliers for each of the multiple requisitions associated with the requisition data or each of the multiple projects associated with the project data, or 
 identifying a set of similar projects for each of the multiple projects associated with the project data; and 
 
 perform, after generating the set of recommendations, one or more actions. 
   
     
     
         16 . The non-transitory computer-readable medium of  claim 15 , wherein the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:
 determine a set of scores for each of the multiple invoices, each of the multiple requisitions, or each of the multiple projects based on output from the set of machine learning models,
 wherein the set of scores indicates at least one of:
 the one or more of the multiple categories into which each of the multiple invoices are to be categorized, 
 the set of possible suppliers for each of the requisitions or each of the set of similar projects, or 
 the set of similar projects for each of the multiple projects. 
 
   
     
     
         17 . The non-transitory computer-readable medium of  claim 15 , wherein the one or more instructions, that cause the one or more processors to perform the one or more actions, cause the one or more processors to:
 perform an analysis related to the multiple invoices,   send a message to a system to place an order for one or more items associated with the multiple requisitions, or   send a message to a device associated with an individual associated with a project, of the multiple projects,
 wherein the message includes information that identifies the set of similar projects. 
   
     
     
         18 . The non-transitory computer-readable medium of  claim 15 , wherein the one or more instructions, that cause the one or more processors to process the data using the feature extraction engine, cause the one or more processors to:
 identify a subset of the data to be analyzed based on a set of terms identified in the data; and   normalize, after identifying the subset of the data, the subset of the data to a set of pre-determined terms by mapping the terms to the set of pre-determined terms.   
     
     
         19 . The non-transitory computer-readable medium of  claim 15 , wherein the one or more instructions, that cause the one or more processors to process the data using the transformation engine, cause the one or more processors to:
 classify the data into one or more classifications after processing the data using the feature extraction engine,
 wherein the one or more classifications are associated with generalizing the data; and 
   process, in association with classifying the data, the data using a hash function,
 wherein the hash function is associated with compressing the data. 
   
     
     
         20 . The non-transitory computer-readable medium of  claim 15 , wherein the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:
 receive the set of machine learning models from a server device prior to processing the data using the set of machine learning models.